Error bounds on the output of artificial neural networks
Conference
·
· Transactions of the American Nuclear Society; (United States)
OSTI ID:6903121
- Iowa State Univ., Ames, IA (United States)
Resolving the uncertainties associated with solutions obtained from artificial neural networks (ANNs) is a major concern for ANN researchers. Error bounds on the solutions are important because they are an integral part of verification and validation. In this research, stacked generalization (SG) is applied to provide error bounds for novel solutions obtained from ANNS. An outline of SG and its use is given. The data used in this demonstration of SG are given. This work shows that SG can provide error bounds on ANN results. We have applied SG to nuclear power plant fault detection for verification of diagnoses provided by ANNs.
- OSTI ID:
- 6903121
- Report Number(s):
- CONF-931160--
- Journal Information:
- Transactions of the American Nuclear Society; (United States), Journal Name: Transactions of the American Nuclear Society; (United States) Vol. 69; ISSN 0003-018X; ISSN TANSAO
- Country of Publication:
- United States
- Language:
- English
Similar Records
Error bounds on the outputs of artificial neural networks
Nuclear power plant fault-diagnosis using neural networks with error estimation
Nuclear power plant fault-diagnosis using artificial neural networks
Conference
·
Thu Dec 30 23:00:00 EST 1993
·
OSTI ID:206442
Nuclear power plant fault-diagnosis using neural networks with error estimation
Journal Article
·
Fri Dec 30 23:00:00 EST 1994
· Transactions of the American Nuclear Society
·
OSTI ID:89046
Nuclear power plant fault-diagnosis using artificial neural networks
Conference
·
Wed Dec 30 23:00:00 EST 1992
·
OSTI ID:10140171